Microorganisms play an integral and often unique role in ecosystem functions yet people know little about the dominant populations that presumably play vital roles in these functions, nor do people know much about how these populations differ with habitat. The greatest microbial diversity at small scales appears to reside in the soil, and hence soil microbial communities are among the most complex, diverse, and important assemblages in the biosphere. Analysis of genetic diversity in soil communities by DNA renaturation suggests that there are approximately 4-7×103 different genome equivalents per 30 g of soil, which, if extrapolated to species diversity, implies that there are at least 103 or more species per g of soil.
Understanding the structure and composition of microbial communities and their responses to environmental perturbations such as toxic contamination, climate change, and agricultural and industrial practices is critical for the maintenance and restoration of desirable ecosystem functions. However, due to such extremely high diversity, the detection, characterization, and quantification of microbial communities in environmental samples are formidable tasks for environmental biologists. Traditional culture-based enrichment techniques for studying microbial communities have proven difficult and ultimately, provide an extremely limited view of microbial community diversity and dynamics, because the majority of naturally occurring species can not be cultured. The development and application of nucleic acid-based techniques largely eliminated the reliance on cultivation-dependent methods and consequently, greatly advanced the detection and characterization of microorganisms in natural habitats. However, the limitations of conventional nucleic acid-based detection methods prevent them from being readily adapted as high-throughput, cost-effective assessment tools for monitoring microbial communities.
DNA- or oligonucleotide-based microarray technology is a powerful functional genomics tool that allows researchers to view the physiology of a living cell from a comprehensive and dynamic molecular perspective (e.g., DeRisi et al. 1997, Khodursky et al. 2000, Spellman et al. 1998, Tao et al. 1999, Wei et al. 2001, Wodicka et al. 1997, and Ye et al. 2000). Compared to traditional nucleic acid hybridization with porous membranes, glass slide-based microarrays offer the additional advantages of high density, high sensitivity, rapid (“real-time”) detection, lower cost, automation, and low background levels (Shalon et al. 1996). Target functional genes in environments tend to be highly diverse, and it is difficult, sometimes even experimentally impossible, to identify conserved DNA sequence regions for designing oligonucleotide probes for hybridization or primers for polymerase chain reaction (PCR) amplification. The microarray-based approach, however, does not require such sequence conservation, because all of the diverse gene sequences from different populations of the same functional group can be fabricated on arrays and used as probes to monitor their corresponding distributions in environmental samples.
Although microarray technology has been used successfully to analyze global gene expression in pure cultures (Lockhart et al. 1996, DeRisi et al. 1997, Schena et al. 1996, Richmond et al. 1999, Ye et al. 2000, Thompson et al. 2002, Liu et al. 2003, and Wodicka et al. 1997), it is not clear whether it can be successfully adapted for use in environmental studies with sufficient specificity, sensitivity, and quantitative power (Zhou and Thompson 2002). First, in environmental samples, target and probe sequences can be very diverse, and it is not clear whether the performance of microarrays used with diverse environmental samples is similar to that with pure culture samples and how sequence divergence affects microarray hybridization. Second, unlike pure cultures, environmental samples are generally contaminated with substances such as humic matter, organic contaminants, and metals, which may interfere with nucleic acids-based molecular detection. Third, in contrast to pure cultures, the retrievable biomass in environmental samples is generally low. It is not clear whether microarray hybridization is sensitive enough for detecting microorganisms in environmental samples. Finally, since microarray-based hybridization has inherently high variability, it is uncertain whether microarray-based detection can be quantitative. Environmental and ecological studies often require experimental tools that not only detect the presence or absence of particular groups of microorganisms but also provide quantitative data on their in situ biological activities.
Recently, various microarray formats such as functional gene arrays (Wu et al. 2001, Taroncher-Oldenburg et al. 2003, and Rhee et al. 2004, all of which are herein incorporated by reference in their entirety), community genome arrays (Wu et al. 2004 and Zhou, 2003, both of which are herein incorporated by reference in their entirety), and oligonucleotide arrays (Guschin et al. 1997a, Guschin et al. 1997b, Small et al, 2001, Rudi et al. 2000, Urakawa et al. 2002, Loy et al. 2002, Straub et al. 2002, and Wilson et al. 2002, all of which are herein incorporated by reference in their entirety) have been developed and for bacterial detection and microbial community analyses of environmental samples. However, as described above, these methods do not have sufficient sensitivity to allow quantitative analysis, especially the less abundant microorganisms, of the microbial communities. For example, the method disclosed in Rhee et al. 2004 requires DNAs from at least about 107 cells to achieve reasonably strong hybridization using the 50-mer-based oligonucleotide microarrays.
Novel microarray-based methods that will allow quantitative and sensitive analysis of microbial communities, especially less abundant populations in natural environments, are desirable.